Matt W. Email & Phone Number

San Jose, CA, US

Currently serving as Director of ML Engineering at Automation Anywhere where I lead Machine Learning and Engineering for emerging technology throughout the company.

San Jose, CA, US

Director of Data Science at Automation Anywhere, where I lead integration of generative AI and advanced AI capabilities across our enterprise automation platform.My team is embedded in various AI-focused projects, driving the seamless adoption of machine learning solutions to enhance automation and user experience across the organization.Additionally, I.

San Francisco, California, US

San Francisco, California, US

• Increased model inference speed 1700% by creating a distributed system that leveraged Kubernetes and asynchronous messaging within google cloud
• Improved clustering accuracy five-fold by fine-tuning a BERT transformer and CNN-based models to combine image and text information for prediction.
• Worked constantly with data science and engineering leaders to drive the adoption of key project initiatives.
• Steered data science team to prioritize sales and customer retention in high stakes times to drive additional funding rounds.
• Automated airflow and elasticsearch deployments to development and production clusters

Teaneck, New Jersey, US

• On-site Lead / Data Scientist / Machine Learning Engineer @ Apple Inc.
• Lead a specialized data science team tasked with key machine learning projects, serving as a resource to assist members with individual projects and remain on track for client satisfaction
• Applied machine learning algorithms to automatically find the best answer for customer issues drastically decreasing staff workload by 16,500 hours per month
• Saved over 1000 hours per month by leveraging machine learning with NLP techniques to build a text classification algorithm that predicted with high accuracy whether advisor action was required
• Applied deep learning (seq2seq) models to automate various use cases for translation and summarization
• Slashed anomaly detection time for product issues by more than 10x by incorporating natural language processing, classification, and clustering within a Spark and a Hadoop ecosystem to scale to client needs

Cupertino, California, US

Bloomfield, CT, US

• Performed matched case-control study to identify the effectiveness of different treatment regimens for a specialty condition and provide better financial outcomes for patients
• Claim Severity Predictions - Utilized machine learning techniques (gradient boosting and neural networks) to predict insurance claim severity by employing feature engineering, parameter optimization, and model ensembling

Monterey, CA, US

• Implemented process improvements and automated staffing optimization tasks in R/Shiny for analysts, which increased productivity by over 30%
• Used machine learning to predict staff utilization rates from various features enabling senior management to make real-time adjustments to call routing
• Automated reports and dashboards in Elasticsearch and Kibana which increased real-time and historical analytic capabilities, these were used at all levels of the company, including executive leadership
• Developed analysis tools utilizing data from SQL and NoSQL databases, which streamlined how our team leveraged KPI and operational data

• Designed and implemented Access databases utilizing SQL and VBA which streamlined data accessibility, reduced data redundancy, and more than tripled the efficiency of client processing
• Constructed systems that automated payroll and billing resulting in 90% time savings by management
• Developed numerous tools that analyzed key financial and operational statistics leading to optimized operational efficiency
• Analyzed online search engine and website performance leading to proposing and performing a full redesign of the company website resulting in 5x website traffic and 2x more customers

Master Of Science (M.S.), Advanced Analytics

B.S, Statistical Science